1
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Won C, Yim SS. Emerging methylation-based approaches in microbiome engineering. BIOTECHNOLOGY FOR BIOFUELS AND BIOPRODUCTS 2024; 17:96. [PMID: 38987811 DOI: 10.1186/s13068-024-02529-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 06/10/2024] [Indexed: 07/12/2024]
Abstract
Bacterial epigenetics, particularly through DNA methylation, exerts significant influence over various biological processes such as DNA replication, uptake, and gene regulation in bacteria. In this review, we explore recent advances in characterizing bacterial epigenomes, accompanied by emerging strategies that harness bacterial epigenetics to elucidate and engineer diverse bacterial species with precision and effectiveness. Furthermore, we delve into the potential of epigenetic modifications to steer microbial functions and influence community dynamics, offering promising opportunities for understanding and modulating microbiomes. Additionally, we investigate the extensive diversity of DNA methyltransferases and emphasize their potential utility in the context of the human microbiome. In summary, this review highlights the potential of DNA methylation as a powerful toolkit for engineering microbiomes.
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Affiliation(s)
- Changhee Won
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Sung Sun Yim
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
- Graduate School of Engineering Biology, KAIST, Daejeon, Republic of Korea.
- KAIST Institute for BioCentury, KAIST, Daejeon, Republic of Korea.
- Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, Republic of Korea.
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2
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Joshi SHN, Jenkins C, Ulaeto D, Gorochowski TE. Accelerating Genetic Sensor Development, Scale-up, and Deployment Using Synthetic Biology. BIODESIGN RESEARCH 2024; 6:0037. [PMID: 38919711 PMCID: PMC11197468 DOI: 10.34133/bdr.0037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 04/23/2024] [Indexed: 06/27/2024] Open
Abstract
Living cells are exquisitely tuned to sense and respond to changes in their environment. Repurposing these systems to create engineered biosensors has seen growing interest in the field of synthetic biology and provides a foundation for many innovative applications spanning environmental monitoring to improved biobased production. In this review, we present a detailed overview of currently available biosensors and the methods that have supported their development, scale-up, and deployment. We focus on genetic sensors in living cells whose outputs affect gene expression. We find that emerging high-throughput experimental assays and evolutionary approaches combined with advanced bioinformatics and machine learning are establishing pipelines to produce genetic sensors for virtually any small molecule, protein, or nucleic acid. However, more complex sensing tasks based on classifying compositions of many stimuli and the reliable deployment of these systems into real-world settings remain challenges. We suggest that recent advances in our ability to precisely modify nonmodel organisms and the integration of proven control engineering principles (e.g., feedback) into the broader design of genetic sensing systems will be necessary to overcome these hurdles and realize the immense potential of the field.
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Affiliation(s)
| | - Christopher Jenkins
- CBR Division, Defence Science and Technology Laboratory, Porton Down, Wiltshire SP4 0JQ, UK
| | - David Ulaeto
- CBR Division, Defence Science and Technology Laboratory, Porton Down, Wiltshire SP4 0JQ, UK
| | - Thomas E. Gorochowski
- School of Biological Sciences, University of Bristol, Bristol BS8 1TQ, UK
- BrisEngBio,
School of Chemistry, University of Bristol, Bristol BS8 1TS, UK
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3
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Xia Y, Du X, Liu B, Guo S, Huo YX. Species-specific design of artificial promoters by transfer-learning based generative deep-learning model. Nucleic Acids Res 2024; 52:6145-6157. [PMID: 38783063 PMCID: PMC11194083 DOI: 10.1093/nar/gkae429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 04/04/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024] Open
Abstract
Native prokaryotic promoters share common sequence patterns, but are species dependent. For understudied species with limited data, it is challenging to predict the strength of existing promoters and generate novel promoters. Here, we developed PromoGen, a collection of nucleotide language models to generate species-specific functional promoters, across dozens of species in a data and parameter efficient way. Twenty-seven species-specific models in this collection were finetuned from the pretrained model which was trained on multi-species promoters. When systematically compared with native promoters, the Escherichia coli- and Bacillus subtilis-specific artificial PromoGen-generated promoters (PGPs) were demonstrated to hold all distribution patterns of native promoters. A regression model was developed to score generated either by PromoGen or by another competitive neural network, and the overall score of PGPs is higher. Encouraged by in silico analysis, we further experimentally characterized twenty-two B. subtilis PGPs, results showed that four of tested PGPs reached the strong promoter level while all were active. Furthermore, we developed a user-friendly website to generate species-specific promoters for 27 different species by PromoGen. This work presented an efficient deep-learning strategy for de novo species-specific promoter generation even with limited datasets, providing valuable promoter toolboxes especially for the metabolic engineering of understudied microorganisms.
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Affiliation(s)
- Yan Xia
- Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Xiaowen Du
- Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Bin Liu
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China
| | - Shuyuan Guo
- Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Yi-Xin Huo
- Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, Beijing 100081, China
- Tangshan Research Institute, Beijing Institute of Technology, Hebei 063611, China
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4
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Gilliot PA, Gorochowski TE. Transfer learning for cross-context prediction of protein expression from 5'UTR sequence. Nucleic Acids Res 2024:gkae491. [PMID: 38864396 DOI: 10.1093/nar/gkae491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 04/28/2024] [Accepted: 05/28/2024] [Indexed: 06/13/2024] Open
Abstract
Model-guided DNA sequence design can accelerate the reprogramming of living cells. It allows us to engineer more complex biological systems by removing the need to physically assemble and test each potential design. While mechanistic models of gene expression have seen some success in supporting this goal, data-centric, deep learning-based approaches often provide more accurate predictions. This accuracy, however, comes at a cost - a lack of generalization across genetic and experimental contexts that has limited their wider use outside the context in which they were trained. Here, we address this issue by demonstrating how a simple transfer learning procedure can effectively tune a pre-trained deep learning model to predict protein translation rate from 5' untranslated region (5'UTR) sequence for diverse contexts in Escherichia coli using a small number of new measurements. This allows for important model features learnt from expensive massively parallel reporter assays to be easily transferred to new settings. By releasing our trained deep learning model and complementary calibration procedure, this study acts as a starting point for continually refined model-based sequence design that builds on previous knowledge and future experimental efforts.
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Affiliation(s)
- Pierre-Aurélien Gilliot
- School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol BS8 1TQ, UK
| | - Thomas E Gorochowski
- School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol BS8 1TQ, UK
- BrisEngBio, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, UK
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5
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Lin J, Wang X, Liu T, Teng Y, Cui W. Diffusion-Based Generative Network for de Novo Synthetic Promoter Design. ACS Synth Biol 2024; 13:1513-1522. [PMID: 38613497 DOI: 10.1021/acssynbio.4c00041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/15/2024]
Abstract
Computer-aided promoter design is a major development trend in synthetic promoter engineering. Various deep learning models have been used to evaluate or screen synthetic promoters, but there have been few works on de novo promoter design. To explore the potential ability of generative models in promoter design, we established a diffusion-based generative model for promoter design in Escherichia coli. The model was completely driven by sequence data and could study the essential characteristics of natural promoters, thus generating synthetic promoters similar to natural promoters in structure and component. We also improved the calculation method of FID indicator, using a convolution layer to extract the feature matrix of the promoter sequence instead. As a result, we got an FID equal to 1.37, which meant synthetic promoters have a distribution similar to that of natural ones. Our work provides a fresh approach to de novo promoter design, indicating that a completely data-driven generative model is feasible for promoter design.
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Affiliation(s)
- Jianfeng Lin
- School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
| | - Xin Wang
- School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
| | - Tuoyu Liu
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Yue Teng
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China
| | - Wei Cui
- School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
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6
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Zheng L, Shen J, Chen R, Hu Y, Zhao W, Leung ELH, Dai L. Genome engineering of the human gut microbiome. J Genet Genomics 2024; 51:479-491. [PMID: 38218395 DOI: 10.1016/j.jgg.2024.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 01/02/2024] [Accepted: 01/03/2024] [Indexed: 01/15/2024]
Abstract
The human gut microbiome, a complex ecosystem, significantly influences host health, impacting crucial aspects such as metabolism and immunity. To enhance our comprehension and control of the molecular mechanisms orchestrating the intricate interplay between gut commensal bacteria and human health, the exploration of genome engineering for gut microbes is a promising frontier. Nevertheless, the complexities and diversities inherent in the gut microbiome pose substantial challenges to the development of effective genome engineering tools for human gut microbes. In this comprehensive review, we provide an overview of the current progress and challenges in genome engineering of human gut commensal bacteria, whether executed in vitro or in situ. A specific focus is directed towards the advancements and prospects in cargo DNA delivery and high-throughput techniques. Additionally, we elucidate the immense potential of genome engineering methods to enhance our understanding of the human gut microbiome and engineer the microorganisms to enhance human health.
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Affiliation(s)
- Linggang Zheng
- Dr Neher's Biophysics Laboratory for Innovative Drug Discovery/State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macau 999078, China; CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Juntao Shen
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Ruiyue Chen
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yucan Hu
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Zhao
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Elaine Lai-Han Leung
- Cancer Center, Faculty of Health Science, University of Macau, Macau 999078, China; MOE Frontiers Science Center for Precision Oncology, University of Macau, Macau 999078, China.
| | - Lei Dai
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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7
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Andreani V, South EJ, Dunlop MJ. Generating information-dense promoter sequences with optimal string packing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.01.565124. [PMID: 37961203 PMCID: PMC10635063 DOI: 10.1101/2023.11.01.565124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Dense arrangements of binding sites within nucleotide sequences can collectively influence downstream transcription rates or initiate biomolecular interactions. For example, natural promoter regions can harbor many overlapping transcription factor binding sites that influence the rate of transcription initiation. Despite the prevalence of overlapping binding sites in nature, rapid design of nucleotide sequences with many overlapping sites remains a challenge. Here, we show that this is an NP-hard problem, coined here as the nucleotide String Packing Problem (SPP). We then introduce a computational technique that efficiently assembles sets of DNA-protein binding sites into dense, contiguous stretches of double-stranded DNA. For the efficient design of nucleotide sequences spanning hundreds of base pairs, we reduce the SPP to an Orienteering Problem with integer distances, and then leverage modern integer linear programming solvers. Our method optimally packs libraries of 20-100 binding sites into dense nucleotide arrays of 50-300 base pairs in 0.05-10 seconds. Unlike approximation algorithms or meta-heuristics, our approach finds provably optimal solutions. We demonstrate how our method can generate large sets of diverse sequences suitable for library generation, where the frequency of binding site usage across the returned sequences can be controlled by modulating the objective function. As an example, we then show how adding additional constraints, like the inclusion of sequence elements with fixed positions, allows for the design of bacterial promoters. The nucleotide string packing approach we present can accelerate the design of sequences with complex DNA-protein interactions. When used in combination with synthesis and high-throughput screening, this design strategy could help interrogate how complex binding site arrangements impact either gene expression or biomolecular mechanisms in varied cellular contexts. Author Summary The way protein binding sites are arranged on DNA can control the regulation and transcription of downstream genes. Areas with a high concentration of binding sites can enable complex interplay between transcription factors, a feature that is exploited by natural promoters. However, designing synthetic promoters that contain dense arrangements of binding sites is a challenge. The task involves overlapping many binding sites, each typically about 10 nucleotides long, within a constrained sequence area, which becomes increasingly difficult as sequence length decreases, and binding site variety increases. We introduce an approach to design nucleotide sequences with optimally packed protein binding sites, which we call the nucleotide String Packing Problem (SPP). We show that the SPP can be solved efficiently using integer linear programming to identify the densest arrangements of binding sites for a specified sequence length. We show how adding additional constraints, like the inclusion of sequence elements with fixed positions, allows for the design of bacterial promoters. The presented approach enables the rapid design and study of nucleotide sequences with complex, dense binding site architectures.
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8
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Cao L, Kong Y, Fan Y, Ni M, Tourancheau A, Ksiezarek M, Mead EA, Koo T, Gitman M, Zhang XS, Fang G. mEnrich-seq: methylation-guided enrichment sequencing of bacterial taxa of interest from microbiome. Nat Methods 2024; 21:236-246. [PMID: 38177508 DOI: 10.1038/s41592-023-02125-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 11/08/2023] [Indexed: 01/06/2024]
Abstract
Metagenomics has enabled the comprehensive study of microbiomes. However, many applications would benefit from a method that sequences specific bacterial taxa of interest, but not most background taxa. We developed mEnrich-seq (in which 'm' stands for methylation and seq for sequencing) for enriching taxa of interest from metagenomic DNA before sequencing. The core idea is to exploit the self versus nonself differentiation by natural bacterial DNA methylation and rationally choose methylation-sensitive restriction enzymes, individually or in combination, to deplete host and background taxa while enriching targeted taxa. This idea is integrated with library preparation procedures and applied in several applications to enrich (up to 117-fold) pathogenic or beneficial bacteria from human urine and fecal samples, including species that are hard to culture or of low abundance. We assessed 4,601 bacterial strains with mapped methylomes so far and showed broad applicability of mEnrich-seq. mEnrich-seq provides microbiome researchers with a versatile and cost-effective approach for selective sequencing of diverse taxa of interest.
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Affiliation(s)
- Lei Cao
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yimeng Kong
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yu Fan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mi Ni
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alan Tourancheau
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Magdalena Ksiezarek
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edward A Mead
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tonny Koo
- Department of Pathology, Molecular and Cell-based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Melissa Gitman
- Department of Pathology, Molecular and Cell-based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Xue-Song Zhang
- Center for Advanced Biotechnology and Medicine, Rutgers University, New Brunswick, NJ, USA
| | - Gang Fang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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9
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Rondthaler S, Sarker B, Howitz N, Shah I, Andrews LB. Toolbox of Characterized Genetic Parts for Staphylococcus aureus. ACS Synth Biol 2024; 13:103-118. [PMID: 38064657 PMCID: PMC10805105 DOI: 10.1021/acssynbio.3c00325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 10/06/2023] [Accepted: 10/10/2023] [Indexed: 01/23/2024]
Abstract
Staphylococcus aureus is an important clinical bacterium prevalent in human-associated microbiomes and the cause of many diseases. However, S. aureus has been intractable to synthetic biology approaches due to limited characterized genetic parts for this nonmodel Gram-positive bacterium. Moreover, genetic manipulation of S. aureus has relied on cumbersome and inefficient cloning strategies. Here, we report the first standardized genetic parts toolbox for S. aureus, which includes characterized promoters, ribosome binding sites, terminators, and plasmid replicons from a variety of bacteria for precise control of gene expression. We established a standard relative expression unit (REU) for S. aureus using a plasmid reference and characterized genetic parts in standardized REUs using S. aureus ATCC 12600. We constructed promoter and terminator part plasmids that are compatible with an efficient Type IIS DNA assembly strategy to effectively build multipart DNA constructs. A library of 24 constitutive promoters was built and characterized in S. aureus, which showed a 380-fold activity range. This promoter library was also assayed in Bacillus subtilis (122-fold activity range) to demonstrate the transferability of the constitutive promoters between these Gram-positive bacteria. By applying an iterative design-build-test-learn cycle, we demonstrated the use of our toolbox for the rational design and engineering of a tetracycline sensor in S. aureus using the PXyl-TetO aTc-inducible promoter that achieved 25.8-fold induction. This toolbox greatly expands the growing number of genetic parts for Gram-positive bacteria and will allow researchers to leverage synthetic biology approaches to study and engineer cellular processes in S. aureus.
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Affiliation(s)
- Stephen
N. Rondthaler
- Department
of Chemical Engineering, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
| | - Biprodev Sarker
- Department
of Chemical Engineering, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
| | - Nathaniel Howitz
- Department
of Chemical Engineering, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
| | - Ishita Shah
- Department
of Chemical Engineering, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
| | - Lauren B. Andrews
- Department
of Chemical Engineering, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
- Molecular
and Cellular Biology Graduate Program, University
of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
- Biotechnology
Training Program, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
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10
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Zeng M, Sarker B, Howitz N, Shah I, Andrews LB. Synthetic Homoserine Lactone Sensors for Gram-Positive Bacillus subtilis Using LuxR-Type Regulators. ACS Synth Biol 2024; 13:282-299. [PMID: 38079538 PMCID: PMC10805106 DOI: 10.1021/acssynbio.3c00504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 10/11/2023] [Accepted: 10/18/2023] [Indexed: 01/23/2024]
Abstract
A universal biochemical signal for bacterial cell-cell communication could facilitate programming dynamic responses in diverse bacterial consortia. However, the classical quorum sensing paradigm is that Gram-negative and Gram-positive bacteria generally communicate via homoserine lactones (HSLs) or oligopeptide molecular signals, respectively, to elicit population responses. Here, we create synthetic HSL sensors for Gram-positive Bacillus subtilis 168 using allosteric LuxR-type regulators (RpaR, LuxR, RhlR, and CinR) and synthetic promoters. Promoters were combinatorially designed from different sequence elements (-35, -16, -10, and transcriptional start regions). We quantified the effects of these combinatorial promoters on sensor activity and determined how regulator expression affects its activation, achieving up to 293-fold activation. Using the statistical design of experiments, we identified significant effects of promoter regions and pairwise interactions on sensor activity, which helped to understand the sequence-function relationships for synthetic promoter design. We present the first known set of functional HSL sensors (≥20-fold dynamic range) in B. subtilis for four different HSL chemical signals: p-coumaroyl-HSL, 3-oxohexanoyl-HSL, n-butyryl-HSL, and n-(3-hydroxytetradecanoyl)-HSL. This set of synthetic HSL sensors for a Gram-positive bacterium can pave the way for designable interspecies communication within microbial consortia.
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Affiliation(s)
- Min Zeng
- Department
of Chemical Engineering, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
| | - Biprodev Sarker
- Department
of Chemical Engineering, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
| | - Nathaniel Howitz
- Department
of Chemical Engineering, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
| | - Ishita Shah
- Department
of Chemical Engineering, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
| | - Lauren B. Andrews
- Department
of Chemical Engineering, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
- Molecular
and Cellular Biology Graduate Program, University
of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
- Biotechnology
Training Program, University of Massachusetts
Amherst, Amherst, Massachusetts 01003, United States
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11
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Wang X, Xu K, Tan Y, Yu S, Zhao X, Zhou J. Deep Learning-Assisted Design of Novel Promoters in Escherichia coli. ADVANCED GENETICS (HOBOKEN, N.J.) 2023; 4:2300184. [PMID: 38099247 PMCID: PMC10716054 DOI: 10.1002/ggn2.202300184] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 10/09/2023] [Indexed: 12/17/2023]
Abstract
Deep learning (DL) approaches have the ability to accurately recognize promoter regions and predict their strength. Here, the potential for controllably designing active Escherichia coli promoter is explored by combining multiple deep learning models. First, "DRSAdesign," which relies on a diffusion model to generate different types of novel promoters is created, followed by predicting whether they are real or fake and strength. Experimental validation showed that 45 out of 50 generated promoters are active with high diversity, but most promoters have relatively low activity. Next, "Ndesign," which relies on generating random sequences carrying functional -35 and -10 motifs of the sigma70 promoter is introduced, and their strength is predicted using the designed DL model. The DL model is trained and validated using 200 and 50 generated promoters, and displays Pearson correlation coefficients of 0.49 and 0.43, respectively. Taking advantage of the DL models developed in this work, possible 6-mers are predicted as key functional motifs of the sigma70 promoter, suggesting that promoter recognition and strength prediction mainly rely on the accommodation of functional motifs. This work provides DL tools to design promoters and assess their functions, paving the way for DL-assisted metabolic engineering.
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Affiliation(s)
- Xinglong Wang
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of BiotechnologyJiangnan University1800 Lihu RoadWuxiJiangsu214122China
- Science Center for Future FoodsJiangnan University1800 Lihu RoadWuxiJiangsu214122China
| | - Kangjie Xu
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of BiotechnologyJiangnan University1800 Lihu RoadWuxiJiangsu214122China
- Science Center for Future FoodsJiangnan University1800 Lihu RoadWuxiJiangsu214122China
| | - Yameng Tan
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of BiotechnologyJiangnan University1800 Lihu RoadWuxiJiangsu214122China
- Science Center for Future FoodsJiangnan University1800 Lihu RoadWuxiJiangsu214122China
| | - Shangyang Yu
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of BiotechnologyJiangnan University1800 Lihu RoadWuxiJiangsu214122China
- Science Center for Future FoodsJiangnan University1800 Lihu RoadWuxiJiangsu214122China
| | - Xinyi Zhao
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of BiotechnologyJiangnan University1800 Lihu RoadWuxiJiangsu214122China
- Science Center for Future FoodsJiangnan University1800 Lihu RoadWuxiJiangsu214122China
| | - Jingwen Zhou
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of BiotechnologyJiangnan University1800 Lihu RoadWuxiJiangsu214122China
- Science Center for Future FoodsJiangnan University1800 Lihu RoadWuxiJiangsu214122China
- Jiangsu Province Engineering Research Center of Food Synthetic BiotechnologyJiangnan UniversityWuxi214122China
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12
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Fu ZH, He SZ, Wu Y, Zhao GR. Design and deep learning of synthetic B-cell-specific promoters. Nucleic Acids Res 2023; 51:11967-11979. [PMID: 37889080 PMCID: PMC10681721 DOI: 10.1093/nar/gkad930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 09/20/2023] [Accepted: 10/10/2023] [Indexed: 10/28/2023] Open
Abstract
Synthetic biology and deep learning synergistically revolutionize our ability for decoding and recoding DNA regulatory grammar. The B-cell-specific transcriptional regulation is intricate, and unlock the potential of B-cell-specific promoters as synthetic elements is important for B-cell engineering. Here, we designed and pooled synthesized 23 640 B-cell-specific promoters that exhibit larger sequence space, B-cell-specific expression, and enable diverse transcriptional patterns in B-cells. By MPRA (Massively parallel reporter assays), we deciphered the sequence features that regulate promoter transcriptional, including motifs and motif syntax (their combination and distance). Finally, we built and trained a deep learning model capable of predicting the transcriptional strength of the immunoglobulin V gene promoter directly from sequence. Prediction of thousands of promoter variants identified in the global human population shows that polymorphisms in promoters influence the transcription of immunoglobulin V genes, which may contribute to individual differences in adaptive humoral immune responses. Our work helps to decipher the transcription mechanism in immunoglobulin genes and offers thousands of non-similar promoters for B-cell engineering.
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Affiliation(s)
- Zong-Heng Fu
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Frontiers Research Institute for Synthetic Biology, Tianjin University, Tianjin 300072, China
| | - Si-Zhe He
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Frontiers Research Institute for Synthetic Biology, Tianjin University, Tianjin 300072, China
| | - Yi Wu
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Frontiers Research Institute for Synthetic Biology, Tianjin University, Tianjin 300072, China
| | - Guang-Rong Zhao
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
- Frontiers Research Institute for Synthetic Biology, Tianjin University, Tianjin 300072, China
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13
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Zhang P, Wang H, Xu H, Wei L, Liu L, Hu Z, Wang X. Deep flanking sequence engineering for efficient promoter design using DeepSEED. Nat Commun 2023; 14:6309. [PMID: 37813854 PMCID: PMC10562447 DOI: 10.1038/s41467-023-41899-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 09/20/2023] [Indexed: 10/11/2023] Open
Abstract
Designing promoters with desirable properties is essential in synthetic biology. Human experts are skilled at identifying strong explicit patterns in small samples, while deep learning models excel at detecting implicit weak patterns in large datasets. Biologists have described the sequence patterns of promoters via transcription factor binding sites (TFBSs). However, the flanking sequences of cis-regulatory elements, have long been overlooked and often arbitrarily decided in promoter design. To address this limitation, we introduce DeepSEED, an AI-aided framework that efficiently designs synthetic promoters by combining expert knowledge with deep learning techniques. DeepSEED has demonstrated success in improving the properties of Escherichia coli constitutive, IPTG-inducible, and mammalian cell doxycycline (Dox)-inducible promoters. Furthermore, our results show that DeepSEED captures the implicit features in flanking sequences, such as k-mer frequencies and DNA shape features, which are crucial for determining promoter properties.
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Affiliation(s)
- Pengcheng Zhang
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing, China
| | - Haochen Wang
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing, China
| | - Hanwen Xu
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing, China
| | - Lei Wei
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing, China
| | - Liyang Liu
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing, China
| | - Zhirui Hu
- Center for Statistical Science, Tsinghua University, Beijing, China
| | - Xiaowo Wang
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing, China.
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14
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Arnold J, Glazier J, Mimee M. Genetic Engineering of Resident Bacteria in the Gut Microbiome. J Bacteriol 2023; 205:e0012723. [PMID: 37382533 PMCID: PMC10367592 DOI: 10.1128/jb.00127-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2023] Open
Abstract
Techniques by which to genetically manipulate members of the microbiota enable both the evaluation of host-microbe interactions and an avenue by which to monitor and modulate human physiology. Genetic engineering applications have traditionally focused on model gut residents, such as Escherichia coli and lactic acid bacteria. However, emerging efforts by which to develop synthetic biology toolsets for "nonmodel" resident gut microbes could provide an improved foundation for microbiome engineering. As genome engineering tools come online, so too have novel applications for engineered gut microbes. Engineered resident gut bacteria facilitate investigations of the roles of microbes and their metabolites on host health and allow for potential live microbial biotherapeutics. Due to the rapid pace of discovery in this burgeoning field, this minireview highlights advancements in the genetic engineering of all resident gut microbes.
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Affiliation(s)
- Jack Arnold
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois, USA
| | - Joshua Glazier
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois, USA
| | - Mark Mimee
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois, USA
- Department of Microbiology, University of Chicago, Chicago, Illinois, USA
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15
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Pearson AN, Thompson MG, Kirkpatrick LD, Ho C, Vuu KM, Waldburger LM, Keasling JD, Shih PM. The pGinger Family of Expression Plasmids. Microbiol Spectr 2023; 11:e0037323. [PMID: 37212656 PMCID: PMC10269703 DOI: 10.1128/spectrum.00373-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 05/09/2023] [Indexed: 05/23/2023] Open
Abstract
The pGinger suite of expression plasmids comprises 43 plasmids that will enable precise constitutive and inducible gene expression in a wide range of Gram-negative bacterial species. Constitutive vectors are composed of 16 synthetic constitutive promoters upstream of red fluorescent protein (RFP), with a broad-host-range BBR1 origin and a kanamycin resistance marker. The family also has seven inducible systems (Jungle Express, Psal/NahR, Pm/XylS, Prha/RhaS, LacO1/LacI, LacUV5/LacI, and Ptet/TetR) controlling RFP expression on BBR1/kanamycin plasmid backbones. For four of these inducible systems (Jungle Express, Psal/NahR, LacO1/LacI, and Ptet/TetR), we created variants that utilize the RK2 origin and spectinomycin or gentamicin selection. Relevant RFP expression and growth data have been collected in the model bacterium Escherichia coli as well as Pseudomonas putida. All pGinger vectors are available via the Joint BioEnergy Institute (JBEI) Public Registry. IMPORTANCE Metabolic engineering and synthetic biology are predicated on the precise control of gene expression. As synthetic biology expands beyond model organisms, more tools will be required that function robustly in a wide range of bacterial hosts. The pGinger family of plasmids constitutes 43 plasmids that will enable both constitutive and inducible gene expression in a wide range of nonmodel Proteobacteria.
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Affiliation(s)
- Allison N. Pearson
- Joint BioEnergy Institute, Emeryville, California, USA
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
- Department of Plant and Microbial Biology, University of California, Berkeley, California, USA
| | - Mitchell G. Thompson
- Joint BioEnergy Institute, Emeryville, California, USA
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Liam D. Kirkpatrick
- Joint BioEnergy Institute, Emeryville, California, USA
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Cindy Ho
- Joint BioEnergy Institute, Emeryville, California, USA
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Khanh M. Vuu
- Joint BioEnergy Institute, Emeryville, California, USA
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
| | - Lucas M. Waldburger
- Joint BioEnergy Institute, Emeryville, California, USA
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
- Department of Bioengineering, University of California, Berkeley, California, USA
| | - Jay D. Keasling
- Joint BioEnergy Institute, Emeryville, California, USA
- Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California, USA
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
- Center for Synthetic Biochemistry, Institute for Synthetic Biology, Shenzhen Institutes for Advanced Technologies, Shenzhen, China
| | - Patrick M. Shih
- Joint BioEnergy Institute, Emeryville, California, USA
- Department of Plant and Microbial Biology, University of California, Berkeley, California, USA
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
- Innovative Genomics Institute, University of California, Berkeley, California, USA
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16
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Gaudêncio SP, Bayram E, Lukić Bilela L, Cueto M, Díaz-Marrero AR, Haznedaroglu BZ, Jimenez C, Mandalakis M, Pereira F, Reyes F, Tasdemir D. Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation. Mar Drugs 2023; 21:md21050308. [PMID: 37233502 DOI: 10.3390/md21050308] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 05/11/2023] [Accepted: 05/12/2023] [Indexed: 05/27/2023] Open
Abstract
Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.
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Affiliation(s)
- Susana P Gaudêncio
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University Lisbon, 2819-516 Caparica, Portugal
- UCIBIO-Applied Molecular Biosciences Unit, Chemistry Department, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal
| | - Engin Bayram
- Institute of Environmental Sciences, Room HKC-202, Hisar Campus, Bogazici University, Bebek, Istanbul 34342, Turkey
| | - Lada Lukić Bilela
- Department of Biology, Faculty of Science, University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina
| | - Mercedes Cueto
- Instituto de Productos Naturales y Agrobiología-CSIC, 38206 La Laguna, Spain
| | - Ana R Díaz-Marrero
- Instituto de Productos Naturales y Agrobiología-CSIC, 38206 La Laguna, Spain
- Instituto Universitario de Bio-Orgánica (IUBO), Universidad de La Laguna, 38206 La Laguna, Spain
| | - Berat Z Haznedaroglu
- Institute of Environmental Sciences, Room HKC-202, Hisar Campus, Bogazici University, Bebek, Istanbul 34342, Turkey
| | - Carlos Jimenez
- CICA- Centro Interdisciplinar de Química e Bioloxía, Departamento de Química, Facultade de Ciencias, Universidade da Coruña, 15071 A Coruña, Spain
| | - Manolis Mandalakis
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, HCMR Thalassocosmos, 71500 Gournes, Crete, Greece
| | - Florbela Pereira
- LAQV, REQUIMTE, Chemistry Department, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal
| | - Fernando Reyes
- Fundación MEDINA, Avda. del Conocimiento 34, 18016 Armilla, Spain
| | - Deniz Tasdemir
- GEOMAR Centre for Marine Biotechnology (GEOMAR-Biotech), Research Unit Marine Natural Products Chemistry, GEOMAR Helmholtz Centre for Ocean Research Kiel, Am Kiel-Kanal 44, 24106 Kiel, Germany
- Faculty of Mathematics and Natural Science, Kiel University, Christian-Albrechts-Platz 4, 24118 Kiel, Germany
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17
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Ghataora JS, Gebhard S, Reeksting BJ. Chimeric MerR-Family Regulators and Logic Elements for the Design of Metal Sensitive Genetic Circuits in Bacillus subtilis. ACS Synth Biol 2023; 12:735-749. [PMID: 36629785 PMCID: PMC10028694 DOI: 10.1021/acssynbio.2c00545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Whole-cell biosensors are emerging as promising tools for monitoring environmental pollutants such as heavy metals. These sensors constitute a genetic circuit comprising a sensing module and an output module, such that a detectable signal is produced in the presence of the desired analyte. The MerR family of metal-responsive regulators offers great potential for the construction of metal sensing circuits, due to their high sensitivity, tight transcription control, and large diversity in metal-specificity. However, the sensing diversity is broadest in Gram-negative systems, while chassis organisms are often selected from Gram-positive species, particularly sporulating bacilli. This can be problematic, because Gram-negative biological parts, such as promoters, are frequently observed to be nonfunctional in Gram-positive hosts. Herein, we combined construction of synthetic genetic circuits and chimeric MerR regulators, supported by structure-guided design, to generate metal-sensitive biosensor modules that are functional in the biotechnological work-horse species Bacillus subtilis. These chimeras consist of a constant Gram-positive derived DNA-binding domain fused to variable metal binding domains of Gram-negative origins. To improve the specificity of the whole-cell biosensor, we developed a modular "AND gate" logic system based on the B. subtilis two-subunit σ-factor, SigO-RsoA, designed to maximize future use for synthetic biology applications in B. subtilis. This work provides insights into the use of modular regulators, such as the MerR family, in the design of synthetic circuits for the detection of heavy metals, with potentially wider applicability of the approach to other systems and genetic backgrounds.
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Affiliation(s)
- Jasdeep S Ghataora
- Life Sciences Department, Milner Centre for Evolution, University of Bath, Claverton Down, Bath BA2 7AY, United Kingdom
| | - Susanne Gebhard
- Life Sciences Department, Milner Centre for Evolution, University of Bath, Claverton Down, Bath BA2 7AY, United Kingdom
| | - Bianca J Reeksting
- Life Sciences Department, Milner Centre for Evolution, University of Bath, Claverton Down, Bath BA2 7AY, United Kingdom
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18
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Elmore JR, Dexter GN, Baldino H, Huenemann JD, Francis R, Peabody GL, Martinez-Baird J, Riley LA, Simmons T, Coleman-Derr D, Guss AM, Egbert RG. High-throughput genetic engineering of nonmodel and undomesticated bacteria via iterative site-specific genome integration. SCIENCE ADVANCES 2023; 9:eade1285. [PMID: 36897939 PMCID: PMC10005180 DOI: 10.1126/sciadv.ade1285] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 02/01/2023] [Indexed: 05/31/2023]
Abstract
Efficient genome engineering is critical to understand and use microbial functions. Despite recent development of tools such as CRISPR-Cas gene editing, efficient integration of exogenous DNA with well-characterized functions remains limited to model bacteria. Here, we describe serine recombinase-assisted genome engineering, or SAGE, an easy-to-use, highly efficient, and extensible technology that enables selection marker-free, site-specific genome integration of up to 10 DNA constructs, often with efficiency on par with or superior to replicating plasmids. SAGE uses no replicating plasmids and thus lacks the host range limitations of other genome engineering technologies. We demonstrate the value of SAGE by characterizing genome integration efficiency in five bacteria that span multiple taxonomy groups and biotechnology applications and by identifying more than 95 heterologous promoters in each host with consistent transcription across environmental and genetic contexts. We anticipate that SAGE will rapidly expand the number of industrial and environmental bacteria compatible with high-throughput genetics and synthetic biology.
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Affiliation(s)
- Joshua R. Elmore
- Biological Science Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Gara N. Dexter
- Biosciences Division, Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN 37831, USA
| | - Henri Baldino
- Biological Science Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Jay D. Huenemann
- Biosciences Division, Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research, University of Tennessee, Knoxville, TN 37996,USA
| | - Ryan Francis
- Biological Science Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - George L. Peabody
- Biosciences Division, Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN 37831, USA
| | - Jessica Martinez-Baird
- Biosciences Division, Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN 37831, USA
| | - Lauren A. Riley
- Biosciences Division, Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research, University of Tennessee, Knoxville, TN 37996,USA
| | - Tuesday Simmons
- Plant and Microbial Biology Department, University of California, Berkeley, CA 94701, USA
| | - Devin Coleman-Derr
- Plant and Microbial Biology Department, University of California, Berkeley, CA 94701, USA
- Plant Gene Expression Center, USDA-ARS, Albany, CA 94710, USA
| | - Adam M. Guss
- Biosciences Division, Oak Ridge National Laboratory, One Bethel Valley Road, Oak Ridge, TN 37831, USA
| | - Robert G. Egbert
- Biological Science Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
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19
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Genomically mined acoustic reporter genes for real-time in vivo monitoring of tumors and tumor-homing bacteria. Nat Biotechnol 2023:10.1038/s41587-022-01581-y. [PMID: 36593411 DOI: 10.1038/s41587-022-01581-y] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 10/20/2022] [Indexed: 01/03/2023]
Abstract
Ultrasound allows imaging at a much greater depth than optical methods, but existing genetically encoded acoustic reporters for in vivo cellular imaging have been limited by poor sensitivity, specificity and in vivo expression. Here we describe two acoustic reporter genes (ARGs)-one for use in bacteria and one for use in mammalian cells-identified through a phylogenetic screen of candidate gas vesicle gene clusters from diverse bacteria and archaea that provide stronger ultrasound contrast, produce non-linear signals distinguishable from background tissue and have stable long-term expression. Compared to their first-generation counterparts, these improved bacterial and mammalian ARGs produce 9-fold and 38-fold stronger non-linear contrast, respectively. Using these new ARGs, we non-invasively imaged in situ tumor colonization and gene expression in tumor-homing therapeutic bacteria, tracked the progression of tumor gene expression and growth in a mouse model of breast cancer, and performed gene-expression-guided needle biopsies of a genetically mosaic tumor, demonstrating non-invasive access to dynamic biological processes at centimeter depth.
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20
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Chemla Y, Dorfan Y, Yannai A, Meng D, Cao P, Glaven S, Gordon DB, Elbaz J, Voigt CA. Parallel engineering of environmental bacteria and performance over years under jungle-simulated conditions. PLoS One 2022; 17:e0278471. [PMID: 36516154 PMCID: PMC9750038 DOI: 10.1371/journal.pone.0278471] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 11/15/2022] [Indexed: 12/15/2022] Open
Abstract
Engineered bacteria could perform many functions in the environment, for example, to remediate pollutants, deliver nutrients to crops or act as in-field biosensors. Model organisms can be unreliable in the field, but selecting an isolate from the thousands that naturally live there and genetically manipulating them to carry the desired function is a slow and uninformed process. Here, we demonstrate the parallel engineering of isolates from environmental samples by using the broad-host-range XPORT conjugation system (Bacillus subtilis mini-ICEBs1) to transfer a genetic payload to many isolates in parallel. Bacillus and Lysinibacillus species were obtained from seven soil and water samples from different locations in Israel. XPORT successfully transferred a genetic function (reporter expression) into 25 of these isolates. They were then screened to identify the best-performing chassis based on the expression level, doubling time, functional stability in soil, and environmentally-relevant traits of its closest annotated reference species, such as the ability to sporulate and temperature tolerance. From this library, we selected Bacillus frigoritolerans A3E1, re-introduced it to soil, and measured function and genetic stability in a contained environment that replicates jungle conditions. After 21 months of storage, the engineered bacteria were viable, could perform their function, and did not accumulate disruptive mutations.
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Affiliation(s)
- Yonatan Chemla
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Yuval Dorfan
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Adi Yannai
- School of Molecular Cell Biology & Biotechnology, Faculty of Life Science, Tel Aviv University, Tel Aviv, Israel
| | - Dechuan Meng
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Paul Cao
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Sarah Glaven
- Center for Bio/Molecular Science and Engineering, Naval Research Laboratory, Washington, DC, United States of America
| | - D. Benjamin Gordon
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Johann Elbaz
- School of Molecular Cell Biology & Biotechnology, Faculty of Life Science, Tel Aviv University, Tel Aviv, Israel
| | - Christopher A. Voigt
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
- * E-mail:
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21
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Joshi SHN, Yong C, Gyorgy A. Inducible plasmid copy number control for synthetic biology in commonly used E. coli strains. Nat Commun 2022; 13:6691. [PMID: 36335103 PMCID: PMC9637173 DOI: 10.1038/s41467-022-34390-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 10/21/2022] [Indexed: 11/06/2022] Open
Abstract
The ability to externally control gene expression has been paradigm shifting for all areas of biological research, especially for synthetic biology. Such control typically occurs at the transcriptional and translational level, while technologies enabling control at the DNA copy level are limited by either (i) relying on a handful of plasmids with fixed and arbitrary copy numbers; or (ii) require multiple plasmids for replication control; or (iii) are restricted to specialized strains. To overcome these limitations, we present TULIP (TUnable Ligand Inducible Plasmid): a self-contained plasmid with inducible copy number control, designed for portability across various Escherichia coli strains commonly used for cloning, protein expression, and metabolic engineering. Using TULIP, we demonstrate through multiple application examples that flexible plasmid copy number control accelerates the design and optimization of gene circuits, enables efficient probing of metabolic burden, and facilitates the prototyping and recycling of modules in different genetic contexts.
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Affiliation(s)
- Shivang Hina-Nilesh Joshi
- grid.440573.10000 0004 1755 5934Division of Engineering, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Chentao Yong
- grid.440573.10000 0004 1755 5934Division of Engineering, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates ,grid.137628.90000 0004 1936 8753Department of Chemical and Biomolecular Engineering, New York University, New York, NY USA
| | - Andras Gyorgy
- grid.440573.10000 0004 1755 5934Division of Engineering, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
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22
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Scholz SA, Lindeboom CD, Freddolino PL. Genetic context effects can override canonical cis regulatory elements in Escherichia coli. Nucleic Acids Res 2022; 50:10360-10375. [PMID: 36134716 PMCID: PMC9561378 DOI: 10.1093/nar/gkac787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 08/10/2022] [Accepted: 09/02/2022] [Indexed: 11/12/2022] Open
Abstract
Recent experiments have shown that in addition to control by cis regulatory elements, the local chromosomal context of a gene also has a profound impact on its transcription. Although this chromosome-position dependent expression variation has been empirically mapped at high-resolution, the underlying causes of the variation have not been elucidated. Here, we demonstrate that 1 kb of flanking, non-coding synthetic sequences with a low frequency of guanosine and cytosine (GC) can dramatically reduce reporter expression compared to neutral and high GC-content flanks in Escherichia coli. Natural and artificial genetic context can have a similarly strong effect on reporter expression, regardless of cell growth phase or medium. Despite the strong reduction in the maximal expression level from the fully-induced reporter, low GC synthetic flanks do not affect the time required to reach the maximal expression level after induction. Overall, we demonstrate key determinants of transcriptional propensity that appear to act as tunable modulators of transcription, independent of regulatory sequences such as the promoter. These findings provide insight into the regulation of naturally occurring genes and an independent control for optimizing expression of synthetic biology constructs.
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Affiliation(s)
- Scott A Scholz
- Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Chase D Lindeboom
- Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Peter L Freddolino
- Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
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23
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Sridhar S, Ajo-Franklin CM, Masiello CA. A Framework for the Systematic Selection of Biosensor Chassis for Environmental Synthetic Biology. ACS Synth Biol 2022; 11:2909-2916. [PMID: 35961652 PMCID: PMC9486965 DOI: 10.1021/acssynbio.2c00079] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Microbial biosensors sense and report exposures to stimuli, thereby facilitating our understanding of environmental processes. Successful design and deployment of biosensors hinge on the persistence of the microbial host of the genetic circuit, termed the chassis. However, model chassis organisms may persist poorly in environmental conditions. In contrast, non-model organisms persist better in environmental conditions but are limited by other challenges, such as genetic intractability and part unavailability. Here we identify ecological, metabolic, and genetic constraints for chassis development and propose a conceptual framework for the systematic selection of environmental biosensor chassis. We identify key challenges with using current model chassis and delineate major points of conflict in choosing the most suitable organisms as chassis for environmental biosensing. This framework provides a way forward in the selection of biosensor chassis for environmental synthetic biology.
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Affiliation(s)
- Swetha Sridhar
- Systems,
Synthetic, and Physical Biology Graduate Program, Rice University, 6100 Main Street, MS-180, Houston, Texas 77005, United
States,Tel: 713-348-2565.
| | - Caroline M. Ajo-Franklin
- Department
of BioSciences, Rice University, 6100 Main Street, MS-140, Houston, Texas 77005, United States
| | - Caroline A. Masiello
- Department
of BioSciences, Rice University, 6100 Main Street, MS-140, Houston, Texas 77005, United States,Department
of Earth, Environmental, and Planetary Sciences, Rice University, 6100 Main St, MS-126, Houston, Texas 77005, United
States
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24
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Wang Q, Olesen AK, Maccario L, Madsen JS. An easily modifiable conjugative plasmid for studying horizontal gene transfer. Plasmid 2022; 123-124:102649. [PMID: 36100085 DOI: 10.1016/j.plasmid.2022.102649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 09/03/2022] [Accepted: 09/07/2022] [Indexed: 11/16/2022]
Abstract
Horizontal gene transfer is an important mechanism in bacterial evolution and can occur at striking frequencies when mediated by mobile genetic elements. Conjugative plasmids are mobile genetic elements that are main drivers of horizontal transfer and a major facilitator in the spread of antibiotic resistance genes. However, conjugative plasmid models that readily can be genetically modified with the aim to study horizontal transfer are not currently available. The aim of this study was to develop a conjugative plasmid model where the insertion of gene cassettes such as reporter genes (e.g., fluorescent proteins) or antibiotic resistance genes would be efficient and convenient. Here, we introduced a single attTn7 site into the conjugative broad-host-range IncP-1 plasmid pKJK5 in a non-disruptive manner. Furthermore, a version with lower transfer rate and a non-conjugative version of pKJK5-attTn7 were also constructed. The advantage of having the attTn7 sites is that genes of interest can be introduced in a single step with very high success rate using the Tn7 transposition system. In addition, larger genetic fragments can be inserted. To illustrate the efficacy of the constructed pKJK5 plasmids, they were complemented with sfGFP (a gene encoding superfolder green fluorescent protein) in addition to seven different β-lactamase genes representing the four known classes of β-lactamases.
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Affiliation(s)
- Qinqin Wang
- Section of Microbiology, Department of Biology, University of Copenhagen, 2100 Copenhagen, Denmark
| | - Asmus Kalckar Olesen
- Section of Microbiology, Department of Biology, University of Copenhagen, 2100 Copenhagen, Denmark
| | - Lorrie Maccario
- Section of Microbiology, Department of Biology, University of Copenhagen, 2100 Copenhagen, Denmark
| | - Jonas Stenløkke Madsen
- Section of Microbiology, Department of Biology, University of Copenhagen, 2100 Copenhagen, Denmark.
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25
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LaFleur TL, Hossain A, Salis HM. Automated model-predictive design of synthetic promoters to control transcriptional profiles in bacteria. Nat Commun 2022; 13:5159. [PMID: 36056029 PMCID: PMC9440211 DOI: 10.1038/s41467-022-32829-5] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 08/19/2022] [Indexed: 12/22/2022] Open
Abstract
Transcription rates are regulated by the interactions between RNA polymerase, sigma factor, and promoter DNA sequences in bacteria. However, it remains unclear how non-canonical sequence motifs collectively control transcription rates. Here, we combine massively parallel assays, biophysics, and machine learning to develop a 346-parameter model that predicts site-specific transcription initiation rates for any σ70 promoter sequence, validated across 22132 bacterial promoters with diverse sequences. We apply the model to predict genetic context effects, design σ70 promoters with desired transcription rates, and identify undesired promoters inside engineered genetic systems. The model provides a biophysical basis for understanding gene regulation in natural genetic systems and precise transcriptional control for engineering synthetic genetic systems.
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Affiliation(s)
- Travis L LaFleur
- Department of Chemical Engineering, Pennsylvania State University, University Park, PA, 16801, USA
| | - Ayaan Hossain
- Bioinformatics and Genomics, Pennsylvania State University, University Park, PA, 16801, USA
| | - Howard M Salis
- Department of Chemical Engineering, Pennsylvania State University, University Park, PA, 16801, USA.
- Bioinformatics and Genomics, Pennsylvania State University, University Park, PA, 16801, USA.
- Department of Biological Engineering, Pennsylvania State University, University Park, PA, 16801, USA.
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, 16801, USA.
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26
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McBee RM, Lucht M, Mukhitov N, Richardson M, Srinivasan T, Meng D, Chen H, Kaufman A, Reitman M, Munck C, Schaak D, Voigt C, Wang HH. Engineering living and regenerative fungal-bacterial biocomposite structures. NATURE MATERIALS 2022; 21:471-478. [PMID: 34857911 DOI: 10.1038/s41563-021-01123-y] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Accepted: 09/07/2021] [Indexed: 06/13/2023]
Abstract
Engineered living materials could have the capacity to self-repair and self-replicate, sense local and distant disturbances in their environment, and respond with functionalities for reporting, actuation or remediation. However, few engineered living materials are capable of both responsivity and use in macroscopic structures. Here we describe the development, characterization and engineering of a fungal-bacterial biocomposite grown on lignocellulosic feedstocks that can form mouldable, foldable and regenerative living structures. We have developed strategies to make human-scale biocomposite structures using mould-based and origami-inspired growth and assembly paradigms. Microbiome profiling of the biocomposite over multiple generations enabled the identification of a dominant bacterial component, Pantoea agglomerans, which was further isolated and developed into a new chassis. We introduced engineered P. agglomerans into native feedstocks to yield living blocks with new biosynthetic and sensing-reporting capabilities. Bioprospecting the native microbiota to develop engineerable chassis constitutes an important strategy to facilitate the development of living biomaterials with new properties and functionalities.
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Affiliation(s)
- Ross M McBee
- Department of Biological Sciences, Columbia University, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
| | | | - Nikita Mukhitov
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Miles Richardson
- Department of Systems Biology, Columbia University, New York, NY, USA
- Integrated Program in Cellular, Molecular, and Biomedical Studies, Columbia University, New York, NY, USA
| | - Tarun Srinivasan
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | - Dechuan Meng
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Haorong Chen
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Andrew Kaufman
- Department of Systems Biology, Columbia University, New York, NY, USA
| | | | - Christian Munck
- Department of Systems Biology, Columbia University, New York, NY, USA
| | | | - Christopher Voigt
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Harris H Wang
- Department of Systems Biology, Columbia University, New York, NY, USA.
- Department of Pathology and Cell Biology, Columbia University, New York, NY, USA.
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27
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Lagator M, Sarikas S, Steinrueck M, Toledo-Aparicio D, Bollback JP, Guet CC, Tkačik G. Predicting bacterial promoter function and evolution from random sequences. eLife 2022; 11:64543. [PMID: 35080492 PMCID: PMC8791639 DOI: 10.7554/elife.64543] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 01/09/2022] [Indexed: 12/12/2022] Open
Abstract
Predicting function from sequence is a central problem of biology. Currently, this is possible only locally in a narrow mutational neighborhood around a wildtype sequence rather than globally from any sequence. Using random mutant libraries, we developed a biophysical model that accounts for multiple features of σ70 binding bacterial promoters to predict constitutive gene expression levels from any sequence. We experimentally and theoretically estimated that 10–20% of random sequences lead to expression and ~80% of non-expressing sequences are one mutation away from a functional promoter. The potential for generating expression from random sequences is so pervasive that selection acts against σ70-RNA polymerase binding sites even within inter-genic, promoter-containing regions. This pervasiveness of σ70-binding sites implies that emergence of promoters is not the limiting step in gene regulatory evolution. Ultimately, the inclusion of novel features of promoter function into a mechanistic model enabled not only more accurate predictions of gene expression levels, but also identified that promoters evolve more rapidly than previously thought.
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Affiliation(s)
- Mato Lagator
- School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.,Institute of Science and Technology Austria, Klosterneuburg, Austria
| | - Srdjan Sarikas
- Institute of Science and Technology Austria, Klosterneuburg, Austria.,Center for Physiology and Pharmacology, Medical University of Vienna, Klosterneuburg, Austria
| | | | | | - Jonathan P Bollback
- Institute of Integrative Biology, Functional and Comparative Genomics, University of Liverpool, Liverpool, United Kingdom
| | - Calin C Guet
- Institute of Science and Technology Austria, Klosterneuburg, Austria
| | - Gašper Tkačik
- Institute of Science and Technology Austria, Klosterneuburg, Austria
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28
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Armetta J, Schantz-Klausen M, Shepelin D, Vazquez-Uribe R, Bahl MI, Laursen MF, Licht TR, Sommer MO. Escherichia coli Promoters with Consistent Expression throughout the Murine Gut. ACS Synth Biol 2021; 10:3359-3368. [PMID: 34842418 DOI: 10.1021/acssynbio.1c00325] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Advanced microbial therapeutics have great potential as a novel modality to diagnose and treat a wide range of diseases. Yet, to realize this potential, robust parts for regulating gene expression and consequent therapeutic activity in situ are needed. In this study, we characterized the expression level of more than 8000 variants of the Escherichia coli sigma factor 70 (σ70) promoter in a range of different environmental conditions and growth states using fluorescence-activated cell sorting and deep sequencing. Sampled conditions include aerobic and anaerobic culture in the laboratory as well as growth in several locations of the murine gastrointestinal tract. We found that σ70 promoters in E. coli generally maintain consistent expression levels across the murine gut (R2: 0.55-0.85, p value < 1 × 10-5), suggesting a limited environmental influence but a higher variability between in vitro and in vivo expression levels, highlighting the challenges of translating in vitro promoter activity to in vivo applications. Based on these data, we design the Schantzetta library, composed of eight promoters spanning a wide expression range and displaying a high degree of robustness in both laboratory and in vivo conditions (R2 = 0.98, p = 0.000827). This study provides a systematic assessment of the σ70 promoter activity in E. coli as it transits the murine gut leading to the definition of robust expression cassettes that could be a valuable tool for reliable engineering and development of advanced microbial therapeutics.
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Affiliation(s)
- Jeremy Armetta
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Lyngby, Denmark
| | - Michael Schantz-Klausen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Lyngby, Denmark
| | - Denis Shepelin
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Lyngby, Denmark
| | - Ruben Vazquez-Uribe
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Lyngby, Denmark
| | - Martin Iain Bahl
- National Food Institute, Technical University of Denmark, DK-2800 Lyngby, Denmark
| | | | - Tine Rask Licht
- National Food Institute, Technical University of Denmark, DK-2800 Lyngby, Denmark
| | - Morten O.A. Sommer
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Lyngby, Denmark
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29
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Tietze L, Lale R. Importance of the 5' regulatory region to bacterial synthetic biology applications. Microb Biotechnol 2021; 14:2291-2315. [PMID: 34171170 PMCID: PMC8601185 DOI: 10.1111/1751-7915.13868] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 06/03/2021] [Accepted: 06/04/2021] [Indexed: 01/02/2023] Open
Abstract
The field of synthetic biology is evolving at a fast pace. It is advancing beyond single-gene alterations in single hosts to the logical design of complex circuits and the development of integrated synthetic genomes. Recent breakthroughs in deep learning, which is increasingly used in de novo assembly of DNA components with predictable effects, are also aiding the discipline. Despite advances in computing, the field is still reliant on the availability of pre-characterized DNA parts, whether natural or synthetic, to regulate gene expression in bacteria and make valuable compounds. In this review, we discuss the different bacterial synthetic biology methodologies employed in the creation of 5' regulatory regions - promoters, untranslated regions and 5'-end of coding sequences. We summarize methodologies and discuss their significance for each of the functional DNA components, and highlight the key advances made in bacterial engineering by concentrating on their flaws and strengths. We end the review by outlining the issues that the discipline may face in the near future.
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Affiliation(s)
- Lisa Tietze
- PhotoSynLabDepartment of BiotechnologyFaculty of Natural SciencesNorwegian University of Science and TechnologyTrondheimN‐7491Norway
| | - Rahmi Lale
- PhotoSynLabDepartment of BiotechnologyFaculty of Natural SciencesNorwegian University of Science and TechnologyTrondheimN‐7491Norway
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30
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Abstract
The steadfast advance of the synthetic biology field has enabled scientists to use genetically engineered cells, instead of small molecules or biologics, as the basis for the development of novel therapeutics. Cells endowed with synthetic gene circuits can control the localization, timing and dosage of therapeutic activities in response to specific disease biomarkers and thus represent a powerful new weapon in the fight against disease. Here, we conceptualize how synthetic biology approaches can be applied to programme living cells with therapeutic functions and discuss the advantages that they offer over conventional therapies in terms of flexibility, specificity and predictability, as well as challenges for their development. We present notable advances in the creation of engineered cells that harbour synthetic gene circuits capable of biological sensing and computation of signals derived from intracellular or extracellular biomarkers. We categorize and describe these developments based on the cell scaffold (human or microbial) and the site at which the engineered cell exerts its therapeutic function within its human host. The design of cell-based therapeutics with synthetic biology is a rapidly growing strategy in medicine that holds great promise for the development of effective treatments for a wide variety of human diseases.
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31
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Sinyakov AN, Ryabinin VA, Kostina EV. Application of Array-Based Oligonucleotides for Synthesis of Genetic Designs. Mol Biol 2021. [DOI: 10.1134/s0026893321030109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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32
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Park J, Wang HH. Systematic dissection of σ 70 sequence diversity and function in bacteria. Cell Rep 2021; 36:109590. [PMID: 34433066 PMCID: PMC8716302 DOI: 10.1016/j.celrep.2021.109590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 04/19/2021] [Accepted: 08/02/2021] [Indexed: 10/29/2022] Open
Abstract
Primary σ70 factors are key conserved bacterial regulatory proteins that interact with regulatory DNA to control gene expression. It is, however, poorly understood whether σ70 sequence diversity in different bacteria reflects functional differences. Here, we employ comparative and functional genomics to explore the sequence and function relationship of primary σ70. Using multiplex automated genome engineering and deep sequencing (MAGE-seq), we generate a saturation mutagenesis library and high-resolution fitness map of E. coli σ70 in domains 2-4. Mapping natural σ70 sequence diversity to the E. coli σ70 fitness landscape reveals significant predicted fitness deficits across σ70 orthologs. Interestingly, these predicted deficits are larger than observed fitness changes for 15 σ70 orthologs introduced into E. coli. Finally, we use a multiplexed transcriptional reporter assay and RNA sequencing (RNA-seq) to explore functional differences of several σ70 orthologs. This work provides an in-depth analysis of σ70 sequence and function to improve efforts to understand the evolution and engineering potential of this global regulator.
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Affiliation(s)
- Jimin Park
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA; Integrated Program in Cellular, Molecular and Biomedical Studies, Columbia University Irving Medical Center, New York, NY, USA.
| | - Harris H Wang
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA; Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA.
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33
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Park J, Yim SS, Wang HH. High-Throughput Transcriptional Characterization of Regulatory Sequences from Bacterial Biosynthetic Gene Clusters. ACS Synth Biol 2021; 10:1859-1873. [PMID: 34288650 DOI: 10.1021/acssynbio.0c00639] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Recent efforts to sequence, survey, and functionally characterize the diverse biosynthetic capabilities of bacteria have identified numerous Biosynthetic Gene Clusters (BGCs). Genes found within BGCs are typically transcriptionally silent, suggesting their expression is tightly regulated. To better elucidate the underlying mechanisms and principles that govern BGC regulation on a DNA sequence level, we employed high-throughput DNA synthesis and multiplexed reporter assays to build and to characterize a library of BGC-derived regulatory sequences. Regulatory sequence transcription levels were measured in the Actinobacteria Streptomyces albidoflavus J1074, a popular model strain from a genus rich in BGC diversity. Transcriptional activities varied over 1000-fold in range and were used to identify key features associated with expression, including GC content, transcription start sites, and sequence motifs. Furthermore, we demonstrated that transcription levels could be modulated through coexpression of global regulatory proteins. Lastly, we developed and optimized a S. albidoflavus cell-free expression system for rapid characterization of regulatory sequences. This work helps to elucidate the regulatory landscape of BGCs and provides a diverse library of characterized regulatory sequences for rational engineering and activation of cryptic BGCs.
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Affiliation(s)
- Jimin Park
- Department of Systems Biology, Columbia University Irving Medical Center, New York, New York 10032, United States
- Integrated Program in Cellular, Molecular and Biomedical Studies, Columbia University Irving Medical Center, New York, New York 10032, United States
| | - Sung Sun Yim
- Department of Systems Biology, Columbia University Irving Medical Center, New York, New York 10032, United States
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, New York 10032, United States
| | - Harris H. Wang
- Department of Systems Biology, Columbia University Irving Medical Center, New York, New York 10032, United States
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, New York 10032, United States
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34
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Li L, Maclntyre LW, Brady SF. Refactoring biosynthetic gene clusters for heterologous production of microbial natural products. Curr Opin Biotechnol 2021; 69:145-152. [PMID: 33476936 PMCID: PMC8238852 DOI: 10.1016/j.copbio.2020.12.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 12/03/2020] [Accepted: 12/15/2020] [Indexed: 02/08/2023]
Abstract
Microbial natural products (NPs) are of paramount importance in human medicine, animal health and plant crop protection. Large-scale microbial genome and metagenomic mining has revealed tremendous biosynthetic potential to produce new NPs. However a majority of NP biosynthetic gene clusters (BGCs) are functionally inaccessible under standard laboratory conditions. BGC refactoring and heterologous expression provide a promising synthetic biology approach to NP discovery, yield optimization and combinatorial biosynthesis studies. In this review, we summarize the recent advances pertaining to the heterologous production of bacterial and fungal NPs, with an emphasis on next-generation transcriptional regulatory modules, novel BGC refactoring techniques and optimized heterologous hosts.
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Affiliation(s)
- Lei Li
- Laboratory of Genetically Encoded Small Molecules, The Rockefeller University, 1230 York Avenue, New York, NY 10065, United States
| | - Logan W Maclntyre
- Laboratory of Genetically Encoded Small Molecules, The Rockefeller University, 1230 York Avenue, New York, NY 10065, United States
| | - Sean F Brady
- Laboratory of Genetically Encoded Small Molecules, The Rockefeller University, 1230 York Avenue, New York, NY 10065, United States.
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35
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Manrubia S, Cuesta JA, Aguirre J, Ahnert SE, Altenberg L, Cano AV, Catalán P, Diaz-Uriarte R, Elena SF, García-Martín JA, Hogeweg P, Khatri BS, Krug J, Louis AA, Martin NS, Payne JL, Tarnowski MJ, Weiß M. From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics. Phys Life Rev 2021; 38:55-106. [PMID: 34088608 DOI: 10.1016/j.plrev.2021.03.004] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 03/01/2021] [Indexed: 12/21/2022]
Abstract
Understanding how genotypes map onto phenotypes, fitness, and eventually organisms is arguably the next major missing piece in a fully predictive theory of evolution. We refer to this generally as the problem of the genotype-phenotype map. Though we are still far from achieving a complete picture of these relationships, our current understanding of simpler questions, such as the structure induced in the space of genotypes by sequences mapped to molecular structures, has revealed important facts that deeply affect the dynamical description of evolutionary processes. Empirical evidence supporting the fundamental relevance of features such as phenotypic bias is mounting as well, while the synthesis of conceptual and experimental progress leads to questioning current assumptions on the nature of evolutionary dynamics-cancer progression models or synthetic biology approaches being notable examples. This work delves with a critical and constructive attitude into our current knowledge of how genotypes map onto molecular phenotypes and organismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension. As a final goal, this community should aim at deriving an updated picture of evolutionary processes soundly relying on the structural properties of genotype spaces, as revealed by modern techniques of molecular and functional analysis.
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Affiliation(s)
- Susanna Manrubia
- Department of Systems Biology, Centro Nacional de Biotecnología (CSIC), Madrid, Spain; Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain.
| | - José A Cuesta
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain; Departamento de Matemáticas, Universidad Carlos III de Madrid, Leganés, Spain; Instituto de Biocomputación y Física de Sistemas Complejos (BiFi), Universidad de Zaragoza, Spain; UC3M-Santander Big Data Institute (IBiDat), Getafe, Madrid, Spain
| | - Jacobo Aguirre
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain; Centro de Astrobiología, CSIC-INTA, ctra. de Ajalvir km 4, 28850 Torrejón de Ardoz, Madrid, Spain
| | - Sebastian E Ahnert
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, UK; The Alan Turing Institute, British Library, 96 Euston Road, London NW1 2DB, UK
| | | | - Alejandro V Cano
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Pablo Catalán
- Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain; Departamento de Matemáticas, Universidad Carlos III de Madrid, Leganés, Spain
| | - Ramon Diaz-Uriarte
- Department of Biochemistry, Universidad Autónoma de Madrid, Madrid, Spain; Instituto de Investigaciones Biomédicas "Alberto Sols" (UAM-CSIC), Madrid, Spain
| | - Santiago F Elena
- Instituto de Biología Integrativa de Sistemas, I(2)SysBio (CSIC-UV), València, Spain; The Santa Fe Institute, Santa Fe, NM, USA
| | | | - Paulien Hogeweg
- Theoretical Biology and Bioinformatics Group, Utrecht University, the Netherlands
| | - Bhavin S Khatri
- The Francis Crick Institute, London, UK; Department of Life Sciences, Imperial College London, London, UK
| | - Joachim Krug
- Institute for Biological Physics, University of Cologne, Köln, Germany
| | - Ard A Louis
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford, UK
| | - Nora S Martin
- Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, Cambridge, UK; Sainsbury Laboratory, University of Cambridge, Cambridge, UK
| | - Joshua L Payne
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - Marcel Weiß
- Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, Cambridge, UK; Sainsbury Laboratory, University of Cambridge, Cambridge, UK
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36
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Lawson M, Elf J. Imaging-based screens of pool-synthesized cell libraries. Nat Methods 2021; 18:358-365. [PMID: 33589838 DOI: 10.1038/s41592-020-01053-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 12/21/2020] [Indexed: 01/30/2023]
Abstract
Mapping a genetic perturbation to a change in phenotype is at the core of biological research. Advances in microscopy have transformed these studies, but they have largely been confined to examining a few strains or cell lines at a time. In parallel, there has been a revolution in creating synthetic libraries of genetically altered cells with relative ease. Here we describe methods that combine these powerful tools to perform live-cell imaging of pool-generated strain libraries for improved biological discovery.
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Affiliation(s)
- Michael Lawson
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
| | - Johan Elf
- Department of Cell and Molecular Biology Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
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37
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Yuan J, Mo Q, Fan C. New Set of Yeast Vectors for Shuttle Expression in Escherichia coli. ACS OMEGA 2021; 6:7175-7180. [PMID: 33748631 PMCID: PMC7970545 DOI: 10.1021/acsomega.1c00339] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 01/27/2021] [Indexed: 06/12/2023]
Abstract
Promoters that play an essential role in the gene regulation are of particular interest to the synthetic biology communities. Recent advances in high-throughput DNA sequencing have greatly increased the breadth of new genetic parts. The development of promoters with broad host properties could enable rapid phenotyping of genetic constructs in different hosts. In this study, we discovered that the GAL1/10 bidirectional promoter from Saccharomyces cerevisiae could be used for shuttle expression in Escherichia coli. Further investigation revealed that the GAL1/10 bidirectional promoter is subjected to catabolite repression in E. coli. We next constructed a set of Golden-Gate assembly vectors for shuttle expression between S. cerevisiae and E. coli. The utility of shuttle vectors was demonstrated for rapid phenotyping of a multigene pathway for cinnamyl alcohol production. Taken together, our work opens a new avenue for the future development of broad host expression systems between prokaryotic and eukaryotic hosts.
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38
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Del Valle I, Fulk EM, Kalvapalle P, Silberg JJ, Masiello CA, Stadler LB. Translating New Synthetic Biology Advances for Biosensing Into the Earth and Environmental Sciences. Front Microbiol 2021; 11:618373. [PMID: 33633695 PMCID: PMC7901896 DOI: 10.3389/fmicb.2020.618373] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 12/17/2020] [Indexed: 12/26/2022] Open
Abstract
The rapid diversification of synthetic biology tools holds promise in making some classically hard-to-solve environmental problems tractable. Here we review longstanding problems in the Earth and environmental sciences that could be addressed using engineered microbes as micron-scale sensors (biosensors). Biosensors can offer new perspectives on open questions, including understanding microbial behaviors in heterogeneous matrices like soils, sediments, and wastewater systems, tracking cryptic element cycling in the Earth system, and establishing the dynamics of microbe-microbe, microbe-plant, and microbe-material interactions. Before these new tools can reach their potential, however, a suite of biological parts and microbial chassis appropriate for environmental conditions must be developed by the synthetic biology community. This includes diversifying sensing modules to obtain information relevant to environmental questions, creating output signals that allow dynamic reporting from hard-to-image environmental materials, and tuning these sensors so that they reliably function long enough to be useful for environmental studies. Finally, ethical questions related to the use of synthetic biosensors in environmental applications are discussed.
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Affiliation(s)
- Ilenne Del Valle
- Systems, Synthetic, and Physical Biology Graduate Program, Rice University, Houston, TX, United States
| | - Emily M. Fulk
- Systems, Synthetic, and Physical Biology Graduate Program, Rice University, Houston, TX, United States
| | - Prashant Kalvapalle
- Systems, Synthetic, and Physical Biology Graduate Program, Rice University, Houston, TX, United States
| | - Jonathan J. Silberg
- Department of BioSciences, Rice University, Houston, TX, United States
- Department of Bioengineering, Rice University, Houston, TX, United States
- Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX, United States
| | - Caroline A. Masiello
- Department of BioSciences, Rice University, Houston, TX, United States
- Department of Earth, Environmental and Planetary Sciences, Rice University, Houston, TX, United States
- Department of Chemistry, Rice University, Houston, TX, United States
| | - Lauren B. Stadler
- Department of Civil and Environmental Engineering, Rice University, Houston, TX, United States
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39
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Meng D, Mukhitov N, Neitzey D, Lucht M, Schaak DD, Voigt CA. Rapid and simultaneous screening of pathway designs and chassis organisms, applied to engineered living materials. Metab Eng 2021; 66:308-318. [PMID: 33460821 DOI: 10.1016/j.ymben.2021.01.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 12/14/2020] [Accepted: 01/10/2021] [Indexed: 01/22/2023]
Abstract
Achieving a high product titer through pathway optimization often requires screening many combinations of enzymes and genetic parts. Typically, a library is screened in a single chassis that is a model or production organism. Here, we present a technique where the library is first introduced into B. subtilis XPORT, which has the ability to transfer the DNA to many Gram-positive species using an inducible integrated conjugated element (ICE). This approach is demonstrated using a two-gene pathway that converts tyrosine to melanin, a pigment biopolymer that can serve as a protective coating. A library of 18 pathway variants is conjugated by XPORT into 18 species, including those isolated from soil and industrial contaminants. The resulting 324 strains are screened and the highest titer is 1.2 g/L in B. amyloliquefaciens BT16. The strains were evaluated as co-cultures in an industrial process to make mycelia-grown bulk materials, where the bacteria need to be productive in a stressful, spatially non-uniform and dynamic environment. B. subtilis BGSC 3A35 is found to perform well under these conditions and make melanin in the material, which can be seen visually. This approach enables the simultaneous screening of genetic designs and chassis during the build step of metabolic engineering.
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Affiliation(s)
- Dechuan Meng
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Nikita Mukhitov
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Dana Neitzey
- Ecovative Design LLC, 70 Cohoes Avenue, Green Island, NY, 12183, USA
| | - Matthew Lucht
- Ecovative Design LLC, 70 Cohoes Avenue, Green Island, NY, 12183, USA
| | - Damen D Schaak
- Ecovative Design LLC, 70 Cohoes Avenue, Green Island, NY, 12183, USA
| | - Christopher A Voigt
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
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40
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Gilman J, Zulkower V, Menolascina F. Using a Design of Experiments Approach to Inform the Design of Hybrid Synthetic Yeast Promoters. Methods Mol Biol 2021; 2189:1-17. [PMID: 33180289 DOI: 10.1007/978-1-0716-0822-7_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Hybrid promoter engineering takes advantage of the modular nature of eukaryotic promoters by combining discrete promoter motifs to confer novel regulatory function. By combinatorially screening sequence libraries for trans-acting transcriptional operators, activators, repressors and core promoter sequences, it is possible to derive constitutive or inducible promoter collections covering a broad range of expression strengths. However, combinatorial approaches to promoter design can result in highly complex, multidimensional design spaces, which can be experimentally costly to thoroughly explore in vivo. Here, we describe an in silico pipeline for the design of hybrid promoter libraries that employs a Design of Experiments (DoE) approach to reduce experimental burden and efficiently explore the promoter fitness landscape. We also describe a software pipeline to ensure that the designed promoter sequences are compatible with the YTK assembly standard.
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Affiliation(s)
- James Gilman
- Institute for Bioengineering, School of Engineering, University of Edinburgh, Edinburgh, UK
| | - Valentin Zulkower
- Edinburgh Genome Foundry, The University of Edinburgh, Edinburgh, UK
| | - Filippo Menolascina
- Institute for Bioengineering, School of Engineering, University of Edinburgh, Edinburgh, UK.
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41
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Wen JD, Kuo ST, Chou HHD. The diversity of Shine-Dalgarno sequences sheds light on the evolution of translation initiation. RNA Biol 2020; 18:1489-1500. [PMID: 33349119 DOI: 10.1080/15476286.2020.1861406] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Shine-Dalgarno (SD) sequences, the core element of prokaryotic ribosome-binding sites, facilitate mRNA translation by base-pair interaction with the anti-SD (aSD) sequence of 16S rRNA. In contrast to this paradigm, an inspection of thousands of prokaryotic species unravels tremendous SD sequence diversity both within and between genomes, whereas aSD sequences remain largely static. The pattern has led many to suggest unidentified mechanisms for translation initiation. Here we review known translation-initiation pathways in prokaryotes. Moreover, we seek to understand the cause and consequence of SD diversity through surveying recent advances in biochemistry, genomics, and high-throughput genetics. These findings collectively show: (1) SD:aSD base pairing is beneficial but nonessential to translation initiation. (2) The 5' untranslated region of mRNA evolves dynamically and correlates with organismal phylogeny and ecological niches. (3) Ribosomes have evolved distinct usage of translation-initiation pathways in different species. We propose a model portraying the SD diversity shaped by optimization of gene expression, adaptation to environments and growth demands, and the species-specific prerequisite of ribosomes to initiate translation. The model highlights the coevolution of ribosomes and mRNA features, leading to functional customization of the translation apparatus in each organism.
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Affiliation(s)
- Jin-Der Wen
- Institute of Molecular and Cellular Biology, National Taiwan University, Taipei, Taiwan.,Genome and Systems Biology Degree Program, Academia Sinica and National Taiwan University, Taipei, Taiwan
| | - Syue-Ting Kuo
- Department of Life Science, National Taiwan University, Taipei, Taiwan
| | - Hsin-Hung David Chou
- Genome and Systems Biology Degree Program, Academia Sinica and National Taiwan University, Taipei, Taiwan.,Department of Life Science, National Taiwan University, Taipei, Taiwan
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42
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Yim SS, Johns NI, Noireaux V, Wang HH. Protecting Linear DNA Templates in Cell-Free Expression Systems from Diverse Bacteria. ACS Synth Biol 2020; 9:2851-2855. [PMID: 32926785 DOI: 10.1021/acssynbio.0c00277] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Recent advances in cell-free systems have opened up new capabilities in synthetic biology from rapid prototyping of genetic circuits and metabolic pathways to portable diagnostics and biomanufacturing. A current bottleneck in cell-free systems, especially those employing non-E. coli bacterial species, is the required use of plasmid DNA, which can be laborious to construct, clone, and verify. Linear DNA templates offer a faster and more direct route for many cell-free applications, but they are often rapidly degraded in cell-free reactions. In this study, we evaluated GamS from λ-phage, DNA fragments containing Chi-sites, and Ku from Mycobacterium tuberculosis for their ability to protect linear DNA templates in diverse bacterial cell-free systems. We show that these nuclease inhibitors exhibit differential protective activities against endogenous exonucleases in five different cell-free lysates, highlighting their utility for diverse bacterial species. We expect these linear DNA protection strategies will accelerate high-throughput approaches in cell-free synthetic biology.
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Affiliation(s)
- Sung Sun Yim
- Department of Systems Biology, Columbia University, New York, New York 10027, United States
| | - Nathan I. Johns
- Department of Systems Biology, Columbia University, New York, New York 10027, United States
- Integrated Program in Cellular, Molecular, and Biomedical Studies, Columbia University, New York, New York 10027, United States
| | - Vincent Noireaux
- School of Physics and Astronomy, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Harris H. Wang
- Department of Systems Biology, Columbia University, New York, New York 10027, United States
- Department of Pathology and Cell Biology, Columbia University, New York, New York 10027, United States
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43
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Ke J, Wang B, Yoshikuni Y. Microbiome Engineering: Synthetic Biology of Plant-Associated Microbiomes in Sustainable Agriculture. Trends Biotechnol 2020; 39:244-261. [PMID: 32800605 DOI: 10.1016/j.tibtech.2020.07.008] [Citation(s) in RCA: 101] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 07/16/2020] [Accepted: 07/17/2020] [Indexed: 12/28/2022]
Abstract
To support an ever-increasing population, modern agriculture faces numerous challenges that pose major threats to global food and energy security. Plant-associated microbes, with their many plant growth-promoting (PGP) traits, have enormous potential in helping to solve these challenges. However, the results of their use in agriculture have been variable, probably because of poor colonization. Phytomicrobiome engineering is an emerging field of synthetic biology that may offer ways to alleviate this limitation. This review highlights recent advances in both bottom-up and top-down approaches to engineering non-model bacteria and microbiomes to promote beneficial plant-microbe interactions, as well as advances in strategies to evaluate these interactions. Biosafety, biosecurity, and biocontainment strategies to address the environmental concerns associated with field use of synthetic microbes are also discussed.
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Affiliation(s)
- Jing Ke
- US Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Bing Wang
- US Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Yasuo Yoshikuni
- US Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Center for Advanced Bioenergy and Bioproducts Innovation, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Global Institution for Collaborative Research and Education, Hokkaido University, Hokkaido 060-8589, Japan.
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44
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Kim NM, Sinnott RW, Sandoval NR. Transcription factor-based biosensors and inducible systems in non-model bacteria: current progress and future directions. Curr Opin Biotechnol 2020; 64:39-46. [DOI: 10.1016/j.copbio.2019.09.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 09/09/2019] [Accepted: 09/10/2019] [Indexed: 10/25/2022]
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45
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Hossain A, Lopez E, Halper SM, Cetnar DP, Reis AC, Strickland D, Klavins E, Salis HM. Automated design of thousands of nonrepetitive parts for engineering stable genetic systems. Nat Biotechnol 2020; 38:1466-1475. [PMID: 32661437 DOI: 10.1038/s41587-020-0584-2] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 05/31/2020] [Indexed: 01/08/2023]
Abstract
Engineered genetic systems are prone to failure when their genetic parts contain repetitive sequences. Designing many nonrepetitive genetic parts with desired functionalities remains a difficult challenge with high computational complexity. To overcome this challenge, we developed the Nonrepetitive Parts Calculator to rapidly generate thousands of highly nonrepetitive genetic parts from specified design constraints, including promoters, ribosome-binding sites and terminators. As a demonstration, we designed and experimentally characterized 4,350 nonrepetitive bacterial promoters with transcription rates that varied across a 820,000-fold range, and 1,722 highly nonrepetitive yeast promoters with transcription rates that varied across a 25,000-fold range. We applied machine learning to explain how specific interactions controlled the promoters' transcription rates. We also show that using nonrepetitive genetic parts substantially reduces homologous recombination, resulting in greater genetic stability.
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Affiliation(s)
- Ayaan Hossain
- Bioinformatics and Genomics, Pennsylvania State University, University Park, PA, USA
| | - Eriberto Lopez
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, USA
| | - Sean M Halper
- Department of Chemical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Daniel P Cetnar
- Department of Chemical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Alexander C Reis
- Department of Chemical Engineering, Pennsylvania State University, University Park, PA, USA
| | - Devin Strickland
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, USA
| | - Eric Klavins
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, USA
| | - Howard M Salis
- Bioinformatics and Genomics, Pennsylvania State University, University Park, PA, USA. .,Department of Chemical Engineering, Pennsylvania State University, University Park, PA, USA. .,Department of Biological Engineering, Pennsylvania State University, University Park, PA, USA. .,Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA.
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46
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Ho H, Fang JR, Cheung J, Wang HH. Programmable CRISPR-Cas transcriptional activation in bacteria. Mol Syst Biol 2020; 16:e9427. [PMID: 32657546 PMCID: PMC7356669 DOI: 10.15252/msb.20199427] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 06/07/2020] [Accepted: 06/16/2020] [Indexed: 11/30/2022] Open
Abstract
Programmable gene activation enables fine-tuned regulation of endogenous and synthetic gene circuits to control cellular behavior. While CRISPR-Cas-mediated gene activation has been extensively developed for eukaryotic systems, similar strategies have been difficult to implement in bacteria. Here, we present a generalizable platform for screening and selection of functional bacterial CRISPR-Cas transcription activators. Using this platform, we identified a novel CRISPR activator, dCas9-AsiA, that could activate gene expression by more than 200-fold across genomic and plasmid targets with diverse promoters after directed evolution. The evolved dCas9-AsiA can simultaneously mediate activation and repression of bacterial regulons in E. coli. We further identified hundreds of promoters with varying basal expression that could be induced by dCas9-AsiA, which provides a rich resource of genetic parts for inducible gene activation. Finally, we show that dCas9-AsiA can be ported to other bacteria of clinical and bioindustrial relevance, thus enabling bacterial CRISPRa in more application areas. This work expands the toolbox for programmable gene regulation in bacteria and provides a useful resource for future engineering of other bacterial CRISPR-based gene regulators.
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Affiliation(s)
- Hsing‐I Ho
- Department of Systems BiologyColumbia UniversityNew YorkNYUSA
| | - Jennifer R Fang
- Department of Biological SciencesColumbia UniversityNew YorkNYUSA
| | - Jacky Cheung
- Department of Computer Science and BiologyColumbia UniversityNew YorkNYUSA
| | - Harris H Wang
- Department of Systems BiologyColumbia UniversityNew YorkNYUSA
- Department of Pathology and Cell BiologyColumbia UniversityNew YorkNYUSA
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47
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Genome and sequence determinants governing the expression of horizontally acquired DNA in bacteria. ISME JOURNAL 2020; 14:2347-2357. [PMID: 32514119 PMCID: PMC7608860 DOI: 10.1038/s41396-020-0696-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 05/22/2020] [Accepted: 05/28/2020] [Indexed: 01/23/2023]
Abstract
While horizontal gene transfer is prevalent across the biosphere, the regulatory features that enable expression and functionalization of foreign DNA remain poorly understood. Here, we combine high-throughput promoter activity measurements and large-scale genomic analysis of regulatory regions to investigate the cross-compatibility of regulatory elements (REs) in bacteria. Functional characterization of thousands of natural REs in three distinct bacterial species revealed distinct expression patterns according to RE and recipient phylogeny. Host capacity to activate foreign promoters was proportional to their genomic GC content, while many low GC regulatory elements were both broadly active and had more transcription start sites across hosts. The difference in expression capabilities could be explained by the influence of the host GC content on the stringency of the AT-rich canonical σ70 motif necessary for transcription initiation. We further confirm the generalizability of this model and find widespread GC content adaptation of the σ70 motif in a set of 1,545 genomes from all major bacterial phyla. Our analysis identifies a key mechanism by which the strength of the AT-rich σ70 motif relative to a host's genomic GC content governs the capacity for expression of acquired DNA. These findings shed light on regulatory adaptation in the context of evolving genomic composition.
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48
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Planson AG, Sauveplane V, Dervyn E, Jules M. Bacterial growth physiology and RNA metabolism. BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS 2020; 1863:194502. [PMID: 32044462 DOI: 10.1016/j.bbagrm.2020.194502] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 01/17/2020] [Accepted: 02/06/2020] [Indexed: 12/31/2022]
Abstract
Bacteria are sophisticated systems with high capacity and flexibility to adapt to various environmental conditions. Each prokaryote however possesses a defined metabolic network, which sets its overall metabolic capacity, and therefore the maximal growth rate that can be reached. To achieve optimal growth, bacteria adopt various molecular strategies to optimally adjust gene expression and optimize resource allocation according to the nutrient availability. The resulting physiological changes are often accompanied by changes in the growth rate, and by global regulation of gene expression. The growth-rate-dependent variation of the abundances in the cellular machineries, together with condition-specific regulatory mechanisms, affect RNA metabolism and fate and pose a challenge for rational gene expression reengineering of synthetic circuits. This article is part of a Special Issue entitled: RNA and gene control in bacteria, edited by Dr. M. Guillier and F. Repoila.
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Affiliation(s)
- Anne-Gaëlle Planson
- Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, 78350 Jouy-en-Josas, France.
| | - Vincent Sauveplane
- Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, 78350 Jouy-en-Josas, France.
| | - Etienne Dervyn
- Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, 78350 Jouy-en-Josas, France.
| | - Matthieu Jules
- Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, 78350 Jouy-en-Josas, France.
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49
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Hicks M, Bachmann TT, Wang B. Synthetic Biology Enables Programmable Cell-Based Biosensors. Chemphyschem 2020; 21:132-144. [PMID: 31585026 PMCID: PMC7004036 DOI: 10.1002/cphc.201900739] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 10/03/2019] [Indexed: 01/10/2023]
Abstract
Cell-based biosensors offer cheap, portable and simple methods of detecting molecules of interest but have yet to be truly adopted commercially. Issues with their performance and specificity initially slowed the development of cell-based biosensors. With the development of rational approaches to tune response curves, the performance of biosensors has rapidly improved and there are now many biosensors capable of sensing with the required performance. This has stimulated an increased interest in biosensors and their commercial potential. However the reliability, long term stability and biosecurity of these sensors are still barriers to commercial application and public acceptance. Research into overcoming these issues remains active. Here we present the state-of-the-art tools offered by synthetic biology to allow construction of cell-based biosensors with customisable performance to meet the real world requirements in terms of sensitivity and dynamic range and discuss the research progress to overcome the challenges in terms of the sensor stability and biosecurity fears.
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Affiliation(s)
- Maggie Hicks
- School of Biological SciencesUniversity of EdinburghEdinburghUK
- Centre for Synthetic and Systems BiologyUniversity of EdinburghEdinburghUK
| | - Till T. Bachmann
- Infection MedicineEdinburgh Medical School: Biomedical SciencesUniversity of EdinburghEdinburghUK
| | - Baojun Wang
- School of Biological SciencesUniversity of EdinburghEdinburghUK
- Centre for Synthetic and Systems BiologyUniversity of EdinburghEdinburghUK
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50
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Chen Y, Banerjee D, Mukhopadhyay A, Petzold CJ. Systems and synthetic biology tools for advanced bioproduction hosts. Curr Opin Biotechnol 2020; 64:101-109. [PMID: 31927061 DOI: 10.1016/j.copbio.2019.12.007] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 11/27/2019] [Accepted: 12/08/2019] [Indexed: 02/07/2023]
Abstract
The genomic revolution ushered in an era of discovery and characterization of enzymes from novel organisms that fueled engineering of microbes to produce commodity and high-value compounds. Over the past decade advances in synthetic biology tools in recent years contributed to significant progress in metabolic engineering efforts to produce both biofuels and bioproducts resulting in several such related items being brought to market. These successes represent a burgeoning bio-economy; however, significant resources and time are still necessary to progress a system from proof-of-concept to market. In order to fully realize this potential, methods that examine biological systems in a comprehensive, systematic and high-throughput manner are essential. Recent success in synthetic biology has coincided with the development of systems biology and analytical approaches that kept pace and scaled with technology development. Here, we review a selection of systems biology methods and their use in synthetic biology approaches for microbial biotechnology platforms.
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Affiliation(s)
- Yan Chen
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, USA; Agile BioFoundry, Lawrence Berkeley National Laboratory, Emeryville, CA, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Deepanwita Banerjee
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Aindrila Mukhopadhyay
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Christopher J Petzold
- Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, USA; Agile BioFoundry, Lawrence Berkeley National Laboratory, Emeryville, CA, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
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